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Enregistrement W4323353183 · doi:10.1093/jcag/gwac036.170

A170 ADVERSE EVENTS & SEROLOGICAL RESPONSES FOLLOWING SARS-COV-2 VACCINATION IN INDIVIDUALS WITH INFLAMMATORY BOWEL DISEASE

2023· article· en· W4323353183 sur OpenAlexaffabout
Ante Markovinović, Michelle Herauf, Joshua Quan, Lindsay Hracs, Joseph W. Windsor, Nasrin Sharifi, Stephanie Coward, Léa Caplan, Julia Gorospe, Christopher Ma, Remo Panaccione, R Ingram, Jamil N. Kanji, G Tipples, Jessalyn K. Holodinsky, C Berstein, Sasha Bernatsky, Eric I. Benchimol, Gilaad G. Kaplan

Notice bibliographique

RevueJournal of the Canadian Association of Gastroenterology · 2023
Typearticle
Langueen
DomaineMedicine
ThématiqueSARS-CoV-2 and COVID-19 Research
Établissements canadiensUniversity of TorontoMcGill UniversityUniversity of Calgary
Organismes subventionnairesnon disponible
Mots-clésMedicineVaccinationAdverse effectInflammatory bowel diseaseCohortSerologyProspective cohort studyInternal medicineImmunizationImmunologyDiseaseAntibody

Résumé

récupéré en direct d'OpenAlex

Abstract Background The rapid development and distribution of SARS-CoV-2 vaccines has raised concerns surrounding vaccine safety in immunocompromised populations, such as those with inflammatory bowel disease (IBD). Purpose We described adverse events (AEs) following SARS-CoV-2 vaccination in those with IBD and determined relationships between AEs to post-vaccination antibody titres. Method Individuals with IBD from a prospective cohort in Calgary, Canada (n=670) who received a 1st, 2nd, 3rd, and/or 4th dose of a SARS-CoV-2 vaccine (Pfizer-BioNTech, Moderna, and/or AstraZeneca) were interviewed via telephone for AEs using the Adverse Events Following Immunization form. Subsequently, we assessed injection site reaction as a specific AE outcome. Multivariable logistic regression models were used to assess the association between anti-SARS-CoV-2 spike protein antibody (anti-S) levels within 1–12 weeks of vaccination and injection site reaction following 1st, 2nd, and 3rd dose vaccination. Models were adjusted for age, sex, IBD type, IBD medications, vaccine type, and prior COVID-19 infection. Additionally, we evaluated the risk of flare of IBD within 30 days of vaccination via chart review. Result(s) Table 1 describes AEs in individuals with IBD following 1st dose (n=331), 2nd dose (n=331), 3rd dose (n=195), and 4th dose (n=100) of a SARS-CoV-2 vaccine. AEs were reported in 83.3% of participants after 1st dose, 79.1% after 2nd dose, 77.4% after 3rd dose, and 67.0% after 4th dose. Injection site reaction (pain, redness, etc.) was the most common AE (50.8% of AEs), with fatigue and malaise (18.1%), headache and migraine (8.6%), musculoskeletal discomfort (8.2%), and fever and chills (6.5%) also commonly reported. Multivariable logistic regression determined no associations between anti-S concentration and injection site reaction for all doses. Age above 65 years was associated with decreased injection site reaction following 1st and 3rd doses, while female sex and mRNA vaccine type were associated with increased injection site reaction following 1st and 2nd doses. Prior COVID-19 infection, IBD type, and medication class were not associated with injection site reaction with any dose. Only one participant was diagnosed with a severe AE requiring hospitalization: Immune thrombocytopenic purpura (ITP) following 2nd dose of a Pfizer vaccination. No cases of IBD flare occurred within 30 days of vaccination. Image Conclusion(s) AEs following SARS-CoV-2 vaccination are generally mild and become less common with each consecutive dose. Antibody levels following each dose of the vaccine were not associated with injection site reactions. Females, those under 65 years of age, and those administered mRNA vaccines were more likely to experience an injection site reaction. Prior COVID-19 infection, IBD type, and IBD medication class did not predict injection site reactions. Vaccination was not associated with IBD flare within 30 days of vaccination. Please acknowledge all funding agencies by checking the applicable boxes below Other Please indicate your source of funding; Helmsley Disclosure of Interest A. Markovinovic: None Declared, M. Herauf: None Declared, J. Quan: None Declared, L. Hracs: None Declared, J. Windsor: None Declared, N. Sharifi: None Declared, S. Coward: None Declared, L. Caplan: None Declared, J. Gorospe: None Declared, C. Ma Grant / Research support from: Ferring, Pfizer, , Consultant of: AbbVie, Alimentiv, Amgen, Ferring, Pfizer, Takeda, , Speakers bureau of: AbbVie, Alimentiv, Amgen, Ferring, Pfizer, Takeda, R. Panaccione Grant / Research support from: AbbVie, Ferring, Janssen, Pfizer, Takeda, Consultant of: Abbott, AbbVie, Alimentiv, Amgen, Arena, AstraZeneca, Boehringer Ingelheim, Bristol Myers Squibb, Celgene, Celltrion, Cosmos Pharmaceuticals, Eisai, Elan, Eli Lilly, Ferring, Galapagos, Genentech, Gilead Sciences, GlaxoSmithKline, Janssen, Merck, Mylan, Oppilan Pharma, Pandion Therapeutics, Pandion Pharma, Pfizer, Progenity, Protagonist, Roche, Sandoz, Satisfai Health, Schering-Plough, Shire, Sublimity Therapeutics, Takeda, Theravance, UCB, Speakers bureau of: AbbVie, Arena, Celgene, Eli Lilly, Ferring, Gilead Sciences, Janssen, Merck, Pfizer, Roche, Sandoz, Shire, Takeda, R. Ingram: None Declared, J. Kanji: None Declared, G. Tipples: None Declared, J. Holodinsky: None Declared, C. Berstein Grant / Research support from: AbbVie, Amgen, Janssen, Pfizer, Takeda, Speakers bureau of: AbbVie, Janssen, Pfizer, Takeda, D. Mahoney: None Declared, S. Bernatsky: None Declared, E. Benchimol: None Declared, G. Kaplan Grant / Research support from: Ferring, Speakers bureau of: AbbVie, Janssen, Pfizer

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,004
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,004
Score d'incertitude au seuil0,009

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,004
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,027
Tête enseignante GPT0,316
Écart entre enseignants0,289 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission2
Résumé présentoui

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